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Record W4226343030 · doi:10.1109/lawp.2022.3157322

Designing and Modeling of a Dual-Band Rectenna With Compact Dielectric Resonator Antenna

2022· article· en· W4226343030 on OpenAlexaff
Lei Guo, Xuwang Li, Wenjian Sun, Wen‐Wen Yang, Yangping Zhao, Ke Wu

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique Montréal
FundersNational Natural Science Foundation of China
KeywordsRectennaMulti-band deviceRectifier (neural networks)Electrical engineeringRadio frequencyBandwidth (computing)Electronic engineeringResonatorAntenna (radio)Dielectric resonatorOmnidirectional antennaEnergy conversion efficiencyComputer scienceEngineeringTelecommunicationsRectificationVoltage

Abstract

fetched live from OpenAlex

In this letter, a compact dual-band rectenna is modeled and developed for low radio frequency (RF) power harvesting. The proposed theoretical model can provide a comprehensive analysis of the rectenna, including the power conversion efficiency (PCE) of the diode, matching efficiency of the rectifier, and total PCE of the rectenna. In the rectenna design, a very compact omnidirectional dielectric resonator antenna (DRA) is proposed with a broad bandwidth exceeding 40%. Meanwhile, a dual-band rectifier is designed based on a single branch in the low-power range with bandwidths covering the two fifth-generation (5G) frequency bands in China (2.515–2.675 and 3.4–3.6 GHz). It was found that the theoretical model can show a high accuracy with the calculation error within 5% in analyzing the dual-band rectifier. The DRA and the dual-band rectifier are integrated to form a compact dual-band rectenna. It shows a higher PCE than those of the existing multiband designs working at similar low-power levels. It is hoped to be implemented in 5G-enabled Internet of Things (IoT) applications for powering wireless sensor nodes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.197
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2022
Admission routes1
Has abstractyes

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